Description

This repository contains code related to “Selecting time-series hyperparameters with the artificial jackknife”.

This article proposes a generalisation of the delete-d jackknife to solve hyperparameter selection problems for time series. I call it artificial delete-d jackknife to stress that this approach substitutes the classic removal step with a fictitious deletion, wherein observed datapoints are replaced with artificial missing values. This procedure keeps the data order intact and allows plain compatibility with time series. This manuscript shows a simple illustration in which it is applied to regulate high-dimensional elastic-net vector autoregressive moving average (VARMA) models.

Examples

Artificial delete-d jackknife selection

Vector autoregression

> julia ./src/fx_selection.jl true 4 "./VAR_output"

Vector moving average

> julia ./src/fx_selection.jl false 4 "./VMA_output"

Realised error

Vector autoregression

> julia ./src/fx_realised_error.jl true "./VAR_output"

Vector moving average

> julia ./src/fx_realised_error.jl false "./VMA_output"

Citation

If you use this code or build upon it, please use the following (bibtex) citation:

@misc{pellegrino2022selecting,
      title={Selecting time-series hyperparameters with the artificial jackknife}, 
      author={Filippo Pellegrino},
      year={2022},
      eprint={2002.04697},
      archivePrefix={arXiv},
      primaryClass={stat.ME}
}